Penguins and Diamonds plots

Environment Set up

Notes: setting up my R environment by loading the tidyverse and palmerpenguins packages:

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✓ ggplot2 3.3.3     ✓ purrr   0.3.4
## ✓ tibble  3.1.2     ✓ dplyr   1.0.6
## ✓ tidyr   1.1.3     ✓ stringr 1.4.0
## ✓ readr   1.4.0     ✓ forcats 0.5.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library("palmerpenguins")

Take a loot at the penguins dataset

glimpse(penguins)

## Rows: 344
## Columns: 8
## $ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
## $ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
## $ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
## $ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
## $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
## $ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
## $ sex               <fct> male, female, female, NA, female, male, female, male…
## $ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…

Add features to the plot step by step

Try aesthetics:

• color
• shape
• size
• alpha

smooth vs. point vs. jitter

ggplot(data = penguins) + 
  geom_smooth(mapping = aes(x = flipper_length_mm, y = body_mass_g))

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(data = penguins) + 
  geom_smooth(mapping = aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_point(mapping = aes(x = flipper_length_mm, y = body_mass_g))

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(data = penguins) +
  geom_smooth(mapping = aes(x = flipper_length_mm, y = body_mass_g, linetype = species))

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(data = penguins) + 
  geom_jitter(mapping = aes(x = flipper_length_mm, y = body_mass_g))

## Diamonds Dataset

ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut))

ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut, color = cut))

ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut, fill = cut))

ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut, fill = clarity))

Use facet_wrap for both datasets

ggplot(data = penguins) + 
  geom_point(mapping = aes(x = flipper_length_mm, y = body_mass_g, color = species)) +
  facet_wrap(~species)

ggplot(data = penguins, aes(x = flipper_length_mm, y = body_mass_g)) + 
  geom_point(aes(color = species)) + facet_wrap(~species)

ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = color, fill = cut)) + facet_wrap(~cut)

How facet_grid offers for the penguins dataset

ggplot(data = penguins) + 
  geom_point(mapping = aes(x = flipper_length_mm, y = body_mass_g, color = species)) +
  facet_grid(sex~species)

ggplot(data = penguins) + 
  geom_point(mapping = aes(x = flipper_length_mm, y = body_mass_g, color = species)) +
  facet_grid(~species)

ggplot(data = penguins) + 
  geom_point(mapping = aes(x = flipper_length_mm, y = body_mass_g, color = species)) +
  facet_grid(~sex)

Add titles, subtitles, captions, and annotations step by step